code: def add_category_cols(coef_summary, pred_category):
      """Adds indicators columns to coefficient summary df for categaries.
  
      Parameters
      ----------
      coef_summary : `pandas.DataFrame`
          The coefficient summary df.
          This is typically generated by
          `~greykite.algo.common.model_summary_utils.add_model_coef_df`
          or
          `~greykite.algo.common.model_summary_utils.add_model_coef_df`.
      pred_category : `dict`
          The predictor category dictionary by
          `~greykite.algo.common.col_name_utils.create_pred_category`.
  
      Returns
      -------
      coef_summary_with_new_columns : `pandas.DataFrame
          New df with the following columns added:
  
              "is_intercept" : 0 or 1
                  Intercept or not.
              "is_time_feature" : 0 or 1
                  Time features or not.
                  Time features belong to `~greykite.common.constants.TimeFeaturesEnum`.
              "is_event" : 0 or 1
                  Event features or not.
                  Event features have `~greykite.common.constants.EVENT_PREFIX`.
              "is_trend" : 0 or 1
                  Trend features or not.
                  Trend features have `~greykite.common.constants.CHANGEPOINT_COL_PREFIX` or "cp\\d".
              "is_seasonality" : 0 or 1
                  Seasonality feature or not.
                  Seasonality features have `~greykite.common.constants.SEASONALITY_REGEX`.
              "is_lag" : 0 or 1
                  Lagged features or not.
                  Lagged features have "lag_".
              "is_regressor" : 0 or 1
                  Extra features provided by users.
                  They are provided through ``extra_pred_cols`` in the fit function.
              "is_interaction" : 0 or 1
                  Interaction feature or not.
                  Interaction features have ":".
      """
      coef_summary = coef_summary.copy()
      pred_cols = coef_summary["Pred_col"].tolist()
      coef_summary["is_intercept"] = [
          1 if col in pred_category["intercept"] else 0
          for col in pred_cols
      ]
      coef_summary["is_time_feature"] = [
          1 if col in pred_category["time_features"] else 0
          for col in pred_cols
      ]
      coef_summary["is_event"] = [
          1 if col in pred_category["event_features"] else 0
          for col in pred_cols
      ]
      coef_summary["is_trend"] = [
          1 if col in pred_category["trend_features"] else 0
          for col in pred_cols
      ]
      coef_summary["is_seasonality"] = [
          1 if col in pred_category["seasonality_features"] else 0
          for col in pred_cols
      ]
      coef_summary["is_lag"] = [
          1 if col in pred_category["lag_features"] else 0
          for col in pred_cols
      ]
      coef_summary["is_regressor"] = [
          1 if col in pred_category["regressor_features"] else 0
          for col in pred_cols
      ]
      coef_summary["is_interaction"] = [
          1 if col in pred_category["interaction_features"] else 0
          for col in pred_cols
      ]
      return coef_summary
  
file_path: greykite/greykite/algo/common/col_name_utils.py
imports: import re
  from greykite.common import constants
label_desc: Use image node_id 6 for calling a global function with example usage: add_category_cols(coef_summary, pred_category) and returns: coef_summary